Advances in data-independent acquisition mass spectrometry towards comprehensive digital proteome landscape

Proteogenomics offers a novel avenue in neoantigen identification for cancer immunotherapy

Cancer neoantigens are tumor-specific non-synonymous mutant peptides that activate the immune system to produce an anti-tumor response. Personalized cancer vaccines based on neoantigens are currently one of the most promising therapeutic approaches for cancer treatment. By utilizing the unique mutations within each patient's tumor, these vaccines aim to elicit a strong and specific immune response against cancer cells. However, the identification of neoantigens remains challenging due to the low accuracy of current prediction tools and the high false-positive rate of candidate neoantigens. Since the concept of "proteogenomics" emerged in 2004, it has evolved rapidly with the increased sequencing depth of next-generation sequencing technologies and the maturation of mass spectrometry-based proteomics technologies to become a more comprehensive approach to neoantigen identification, allowing the discovery of high-confidence candidate neoantigens. In this review, we summarize the reason why cancer neoantigens have become attractive targets for immunotherapy, the mechanism of cancer vaccines and the advances in cancer immunotherapy. Considerations relevant to the application emerging of proteogenomics technologies for neoantigen identification and challenges in this field are described.

Exploring Top-Down Mass Spectrometric Approaches To Probe Forest Cobra (Naja melanoleuca) Venom Proteoforms

Snake venoms are comprised of bioactive proteins and peptides that facilitate severe snakebite envenomation symptoms. A comprehensive understanding of venom compositions and the subtle heterogeneity therein is important. While bottom-up proteomics has been the well-established approach to catalogue venom compositions, top-down proteomics has emerged as a complementary strategy to characterize venom heterogeneity at the intact protein level. However, top-down proteomics has not been as widely implemented in the snake venom field as bottom-up proteomics, with various emerging top-down methods yet to be developed for venom systems. Here, we have explored three main top-down mass spectrometry methodologies in a proof-of-concept study to characterize selected three-finger toxin and phospholipase A2 proteoforms from the forest cobra (Naja melanoleuca) venom. We demonstrated the utility of a data-independent acquisition mode "MSE" for untargeted fragmentation on a chromatographic time scale and its improvement in protein sequence coverage compared to conventional targeted tandem mass spectrometry analysis. We also showed that protein identification can be further improved using a hybrid fragmentation approach, combining electron-capture dissociation and collision-induced dissociation. Lastly, we reported the promising application of multifunctional cyclic ion mobility separation and post-ion mobility fragmentation on snake venom proteins for the first time.

Analysis and Visualization of Quantitative Proteomics Data Using FragPipe-Analyst

The FragPipe computational proteomics platform is gaining widespread popularity among the proteomics research community because of its fast processing speed and user-friendly graphical interface. Although FragPipe produces well-formatted output tables that are ready for analysis, there is still a need for an easy-to-use and user-friendly downstream statistical analysis and visualization tool. FragPipe-Analyst addresses this need by providing an R shiny web server to assist FragPipe users in conducting downstream analyses of the resulting quantitative proteomics data. It supports major quantification workflows, including label-free quantification, tandem mass tags, and data-independent acquisition. FragPipe-Analyst offers a range of useful functionalities, such as various missing value imputation options, data quality control, unsupervised clustering, differential expression (DE) analysis using Limma, and gene ontology and pathway enrichment analysis using Enrichr. To support advanced analysis and customized visualizations, we also developed FragPipeAnalystR, an R package encompassing all FragPipe-Analyst functionalities that is extended to support site-specific analysis of post-translational modifications (PTMs). FragPipe-Analyst and FragPipeAnalystR are both open-source and freely available.

Application of molecular networking to improve the compound annotation in liquid chromatography-mass spectrometry-based metabolomics analysis: A case study of Bupleuri radix

INTRODUCTION

Compound annotation is always a challenging step in metabolomics studies. The molecular networking strategy has been developed recently to organize the relationship between compounds as a network based on their tandem mass (MS2) spectra similarity, which can be used to improve compound annotation in metabolomics analysis.

OBJECTIVE

This study used Bupleuri Radix from different geographic areas to evaluate the performance of molecular networking strategy for compound annotation in liquid chromatography-mass spectrometry (LC-MS)-based metabolomics.

METHODOLOGY

The Bupleuri Radix extract was analyzed by LC-quadrupole time-of-flight MS under MSe acquisition mode. After raw data preprocessing, the resulting dataset was used for statistical analysis, including principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA). The chemical makers related to the sample growth place were selected using variable importance in projection (VIP) > 2, fold change (FC) > 2, and p < 0.05. The molecular networking analysis was applied to conduct the compound annotation.

RESULTS

The score plots of PCA showed that the samples were classified into two clusters depending on their growth place. Then, the PLS-DA model was constructed to explore the chemical changes of the samples further. Sixteen compounds were selected as chemical makers and tentatively annotated by the feature-based molecular networking (FBMN) analysis.

CONCLUSION

The results showed that the molecular networking method fully exploits the MS information and is a promising tool for facilitating compound annotation in metabolomics studies. However, the software used for feature extraction influenced the results of library searching and molecular network construction, which need to be taken into account in future studies.

Spatial characterization and stratification of colorectal adenomas by deep visual proteomics

Colorectal adenomas (CRAs) are potential precursor lesions to adenocarcinomas, currently classified by morphological features. We aimed to establish a molecular feature-based risk allocation framework toward improved patient stratification. Deep visual proteomics (DVP) is an approach that combines image-based artificial intelligence with automated microdissection and ultra-high sensitive mass spectrometry. Here, we used DVP on formalin-fixed, paraffin-embedded (FFPE) CRA tissues from nine male patients, immunohistologically stained for caudal-type homeobox 2 (CDX2), a protein implicated in colorectal cancer, enabling the characterization of cellular heterogeneity within distinct tissue regions and across patients. DVP identified DMBT1, MARCKS, and CD99 as protein markers linked to recurrence, suggesting their potential for risk assessment. It also detected a metabolic shift to anaerobic glycolysis in cells with high CDX2 expression. Our findings underscore the potential of spatial proteomics to refine early stage detection and contribute to personalized patient management strategies and provided novel insights into metabolic reprogramming.

In-cell processing enables rapid and in-depth proteome analysis of low-input Caenorhabditis elegans

Caenorhabditis elegans is a widely used genetic model organism, however, the worm cuticle complicates extraction of intracellular proteins, a prerequisite for typical bottom-up proteomics. Conventional physical disruption procedures are not only time-consuming, but can also cause significant sample loss, making it difficult to perform proteomics with low-input samples. Here, for the first time, we present an on-filter in-cell (OFIC) processing approach, which can digest C. elegans proteins directly in the cells of the organism after methanol fixation. With OFIC processing and single-shot LCMS analysis, we identified over 9,400 proteins from a sample of only 200 worms, the largest C. elegans proteome reported to date that did not require fractionation or enrichment. We systematically evaluated the performance of the OFIC approach by comparing it with conventional lysis-based methods. Our data suggest equivalent and unbiased performance of OFIC processing for C. elegans proteome identification and quantitation. We further evaluated the OFIC approach with even lower input samples, then used this method to determine how the proteome is impacted by loss of superoxide dismutase sod-1, the ortholog of human SOD-1, a gene associated with amyotrophic lateral sclerosis (ALS). Analysis of 8,800 proteins from only 50 worms as the initial input showed that loss of sod-1 affects the abundance of proteins required for stress response, ribosome biogenesis, and metabolism. In conclusion, our streamlined OFIC approach, which can be broadly applied to other systems, minimizes sample loss while offering the simplest workflow reported to date for C. elegans proteomics analysis.

PTMoreR-enabled cross-species PTM mapping and comparative phosphoproteomics across mammals

To support PTM proteomic analysis and annotation in different species, we developed PTMoreR, a user-friendly tool that considers the surrounding amino acid sequences of PTM sites during BLAST, enabling a motif-centric analysis across species. By controlling sequence window similarity, PTMoreR can map phosphoproteomic results between any two species, perform site-level functional enrichment analysis, and generate kinase-substrate networks. We demonstrate that the majority of real P-sites in mice can be inferred from experimentally derived human P-sites with PTMoreR mapping. Furthermore, the compositions of 129 mammalian phosphoproteomes can also be predicted using PTMoreR. The method also identifies cross-species phosphorylation events that occur on proteins with an increased tendency to respond to the environmental factors. Moreover, the classic kinase motifs can be extracted across mammalian species, offering an evolutionary angle for refining current motifs. PTMoreR supports PTM proteomics in non-human species and facilitates quantitative phosphoproteomic analysis.

In-Depth Host Cell Protein Analysis and Viral Protein Impurity Monitoring in Adeno-Associated Virus-Based Gene Therapy Products Using Optimized Wide Window Data-Dependent Acquisition Method

Compared to other protein therapeutics, there is currently limited knowledge about the residual host cell proteins (HCPs) in adeno-associated virus (AAV)-based gene therapy products. This is primarily due to the lack of a robust and sensitive mass spectrometry-based method for HCP analysis in AAV samples. Existing liquid chromatography-mass spectrometry methods used for analyzing HCPs in therapeutic monoclonal antibodies (mAbs) often cannot be directly applied to AAVs, due to some unique characteristics of AAV samples encountered during their development such as limited sample availability/protein concentration and the presence of surfactants. In this study, we have developed a novel workflow for robust and in-depth HCP analysis of AAV samples by combining wide-window data-dependent acquisition for improved low-abundance HCP detection with single-pot, solid-phase-enhanced sample preparation (SP3) for low-input sample preparation. Using this newly developed method, we were able to detect more than 650 HCPs in a commercial AAV1 sample with a high quantitative reproducibility. This represents a greater than 5-fold increase in HCP protein identification compared to an in-solution digestion method followed by traditional data-dependent acquisition. Similar benefits can also be achieved for other AAV serotypes that were produced internally and purified through different processes. The detection limit of this method is as low as 0.06 ng/mL, enabling more comprehensive HCP coverage in AAV samples. Moreover, for the first time, we have identified several process-related viral proteins, such as Rep 78 and E4. These proteins need to be closely monitored during AAV process development as they may present a greater risk for immunogenicity compared to HCPs that are derived from human HEK293 cells.

A New Evaluation Metric for Quantitative Accuracy of LC-MS/MS-Based Proteomics with Data-Independent Acquisition

Data-independent acquisition (DIA) has improved the identification and quantitation coverage of peptides and proteins in liquid chromatography-tandem mass spectrometry-based proteomics. However, different DIA data-processing tools can produce very different identification and quantitation results for the same data set. Currently, benchmarking studies of DIA tools are predominantly focused on comparing the identification results, while the quantitative accuracy of DIA measurements is acknowledged to be important but insufficiently investigated, and the absence of suitable metrics for comparing quantitative accuracy is one of the reasons. A new metric is proposed for the evaluation of quantitative accuracy to avoid the influence of differences in false discovery rate control stringency. The part of the quantitation results with high reliability was acquired from each DIA tool first, and the quantitative accuracy was evaluated by comparing quantification error rates at the same number of accurate ratios. From the results of four benchmark data sets, the proposed metric was shown to be more sensitive to discriminating the quantitative performance of DIA tools. Moreover, the DIA tools with advantages in quantitative accuracy were consistently revealed by this metric. The proposed metric can also help researchers in optimizing algorithms of the same DIA tool and sample preprocessing methods to enhance quantitative accuracy.

NCI's Proteomic Data Commons: A Cloud-Based Proteomics Repository Empowering Comprehensive Cancer Analysis through Cross-Referencing with Genomic and Imaging Data

Proteomics has emerged as a powerful tool for studying cancer biology, developing diagnostics, and therapies. With the continuous improvement and widespread availability of high-throughput proteomic technologies, the generation of large-scale proteomic data has become more common in cancer research, and there is a growing need for resources that support the sharing and integration of multi-omics datasets. Such datasets require extensive metadata including clinical, biospecimen, and experimental and workflow annotations that are crucial for data interpretation and reanalysis. The need to integrate, analyze, and share these data has led to the development of NCI's Proteomic Data Commons (PDC), accessible at https://pdc.cancer.gov. As a specialized repository within the NCI Cancer Research Data Commons (CRDC), PDC enables researchers to locate and analyze proteomic data from various cancer types and connect with genomic and imaging data available for the same samples in other CRDC nodes. Presently, PDC houses annotated data from more than 160 datasets across 19 cancer types, generated by several large-scale cancer research programs with cohort sizes exceeding 100 samples (tumor and associated normal when available). In this article, we review the current state of PDC in cancer research, discuss the opportunities and challenges associated with data sharing in proteomics, and propose future directions for the resource.

SIGNIFICANCE

The Proteomic Data Commons (PDC) plays a crucial role in advancing cancer research by providing a centralized repository of high-quality cancer proteomic data, enriched with extensive clinical annotations. By integrating and cross-referencing with complementary genomic and imaging data, the PDC facilitates multi-omics analyses, driving comprehensive insights, and accelerating discoveries across various cancer types.

Pathophysiological insights into HFpEF from studies of human cardiac tissue

Heart failure with preserved ejection fraction (HFpEF) is a major, worldwide health-care problem. Few therapies for HFpEF exist because the pathophysiology of this condition is poorly defined and, increasingly, postulated to be diverse. Although perturbations in other organs contribute to the clinical profile in HFpEF, altered cardiac structure, function or both are the primary causes of this heart failure syndrome. Therefore, studying myocardial tissue is fundamental to improve pathophysiological insights and therapeutic discovery in HFpEF. Most studies of myocardial changes in HFpEF have relied on cardiac tissue from animal models without (or with limited) confirmatory studies in human cardiac tissue. Animal models of HFpEF have evolved based on theoretical HFpEF aetiologies, but these models might not reflect the complex pathophysiology of human HFpEF. The focus of this Review is the pathophysiological insights gained from studies of human HFpEF myocardium. We outline the rationale for these studies, the challenges and opportunities in obtaining myocardial tissue from patients with HFpEF and relevant comparator groups, the analytical approaches, the pathophysiological insights gained to date and the remaining knowledge gaps. Our objective is to provide a roadmap for future studies of cardiac tissue from diverse cohorts of patients with HFpEF, coupling discovery biology with measures to account for pathophysiological diversity.

Comparative proteomics analysis of root and nodule mitochondria of soybean

Legumes perform symbiotic nitrogen fixation through rhizobial bacteroids housed in specialised root nodules. The biochemical process is energy-intensive and consumes a huge carbon source to generate sufficient reducing power. To maintain the symbiosis, malate is supplied by legume nodules to bacteroids as their major carbon and energy source in return for ammonium ions and nitrogenous compounds. To sustain the carbon supply to bacteroids, nodule cells undergo drastic reorganisation of carbon metabolism. Here, a comprehensive quantitative comparison of the mitochondrial proteomes between root nodules and uninoculated roots was performed using data-independent acquisition proteomics, revealing the modulations in nodule mitochondrial proteins and pathways in response to carbon reallocation. Corroborated our findings with that from the literature, we believe nodules preferably allocate cytosolic phosphoenolpyruvates towards malate synthesis in lieu of pyruvate synthesis, and nodule mitochondria prefer malate over pyruvate as the primary source of NADH for ATP production. Moreover, the differential regulation of respiratory chain-associated proteins suggests that nodule mitochondria could enhance the efficiencies of complexes I and IV for ATP synthesis. This study highlighted a quantitative proteomic view of the mitochondrial adaptation in soybean nodules.

High-Throughput Proteomics and Phosphoproteomics of Rat Tissues Using Microflow Zeno SWATH

High-throughput tissue proteomics has great potential in the advancement of precision medicine. Here, we investigated the combined sensitivity of trap-elute microflow liquid chromatography with a ZenoTOF for DIA proteomics and phosphoproteomics. Method optimization was conducted on HEK293T cell lines to determine the optimal variable window size, MS2 accumulation time and gradient length. The ZenoTOF 7600 was then compared to the previous generation TripleTOF 6600 using eight rat organs, finding up to 23% more proteins using a fifth of the sample load and a third of the instrument time. Spectral reference libraries generated from Zeno SWATH data in FragPipe (MSFragger-DIA/DIA-NN) contained 4 times more fragment ions than the DIA-NN only library and quantified more proteins. Replicate single-shot phosphopeptide enrichments of 50-100 μg of rat tryptic peptide were analyzed by microflow HPLC using Zeno SWATH without fractionation. Using Spectronaut we quantified a shallow phosphoproteome containing 1000-3000 phosphoprecursors per organ. Promisingly, clear hierarchical clustering of organs was observed with high Pearson correlation coefficients >0.95 between replicate enrichments and median CV of 20%. The combined sensitivity of microflow HPLC with Zeno SWATH allows for the high-throughput quantitation of an extensive proteome and shallow phosphoproteome from small tissue samples.

MSe Collision Energy Optimization for the Analysis of Membrane Proteins Using HDX-cIMS

Hydrogen/deuterium exchange mass spectrometry (HDX-MS) has evolved as an essential technique in structural proteomics. The use of ion mobility separation (IMS) coupled to HDX-MS has increased the applicability of the technique to more complex systems and has been shown to improve data quality and robustness. The first step when running any HDX-MS workflow is to confirm the sequence and retention time of the peptides resulting from the proteolytic digestion of the nondeuterated protein. Here, we optimized the collision energy ramp of HDMSE experiments for membrane proteins using a Waters SELECT SERIES cIMS-QTOF system following an HDX workflow using Phosphorylase B, XylE transporter, and Smoothened receptor (SMO) as model systems. Although collision energy (CE) ramp 10-50 eV gave the highest amount of positive identified peptides when using Phosphorylase B, XylE, and SMO, results suggest optimal CE ramps are protein specific, and different ramps can produce a unique set of peptides. We recommend cIMS users use different CE ramps in their HDMSE experiments and pool the results to ensure maximum peptide identifications. The results show how selecting an appropriate CE ramp can change the sequence coverage of proteins ranging from 4 to 94%.

Multiplatform tear proteomic profiling reveals novel non-invasive biomarkers for diabetic retinopathy

OBJECTIVES

To investigate a comprehensive proteomic profile of the tear fluid in patients with diabetic retinopathy (DR) and further define non-invasive biomarkers.

METHODS

A cross-sectional, multicentre study that includes 46 patients with DR, 28 patients with diabetes mellitus (DM), and 30 healthy controls (HC). Tear samples were collected with Schirmer strips. As for the discovery set, data-independent acquisition mass spectrometry was used to characterize the tear proteomic profile. Differentially expressed proteins between groups were identified, with gene ontology enrichment analysis and Kyoto Encyclopedia of Genes and Genomes enrichment analysis further developed. Classifying performance of biomarkers for distinguishing DR from DM was compared by the combination of three machine-learning algorithms. The selected biomarker panel was tested in the validation cohort using parallel reaction monitoring mass spectrometry.

RESULTS

Among 3364 proteins quantified, 235 and 88 differentially expressed proteins were identified for DR when compared to HC and DM, respectively, which were fundamentally related to retina homeostasis, inflammation and immunity, oxidative stress, angiogenesis and coagulation, metabolism, and cellular adhesion processes. The biomarker panel consisting of NAD-dependent protein deacetylase sirtuin-2 (SIR2), amine oxidase [flavin-containing] B (AOFB), and U8 snoRNA-decapping enzyme (NUD16) exhibited the best diagnostic performance in discriminating DR from DM, with AUCs of 0.933 and 0.881 in the discovery and validation set, respectively.

CONCLUSIONS

Tear protein dysregulation is comprehensively revealed to be associated with DR onset. The combination of tear SIR2, AOFB, and NUD16 can be a novel potential approach for non-invasive detection or pre-screening of DR.

CLINICAL TRIAL REGISTRATION

Chinese Clinical Trial Registry Identifier: ChiCTR2100054263. https://www.chictr.org.cn/showproj.html?proj=143177 . Date of registration: 2021/12/12.

Mass Spectrometry-Based Proteomics Analysis Unveils PTPRS Inhibits Proliferation and Inflammatory Response of Keratinocytes in Psoriasis

In this study, we used data-independent acquisition-mass spectrometry (DIA-MS) to analyze the serum proteome in psoriasis vulgaris (PsO). The serum proteomes of seven healthy controls and eight patients with PsO were analyzed using DIA-MS. Weighted gene co-expression network analysis was used to identify differentially expressed proteins (DEPs) that were closely related to PsO. Hub proteins of PsO were also identified. The Proteomics Drug Atlas 2023 was used to predict candidate hub protein drugs. To confirm the expression of the candidate factor, protein tyrosine phosphatase receptor S (PTPRS), in psoriatic lesions and the psoriatic keratinocyte model, immunohistochemical staining, quantitative real-time polymerase chain reaction, and western blotting were performed. A total of 129 DEPs were found to be closely related to PsO. The hub proteins for PsO were PVRL1, FGFR1, PTPRS, CDH2, CDH1, MCAM, and THY1. Five candidate hub protein drugs were identified: encorafenib, leupeptin, fedratinib, UNC 0631, and SCH 530348. PTPRS was identified as a common pharmacological target for these five drugs. PTPRS knockdown in keratinocytes promoted the proliferation and expression of IL-1α, IL-1β, IL-23A, TNF-α, MMP9, CXCL8, and S100A9. PTPRS expression was decreased in PsO, and PTPRS negatively regulated PsO. PTPRS may be involved in PsO pathogenesis through the inhibition of keratinocyte proliferation and inflammatory responses and is a potential treatment target for PsO.

Comprehensive Prostate Fluid-Based Spectral Libraries for Enhanced Protein Detection in Urine

Biofluids contain molecules in circulation and from nearby organs that can be indicative of disease states. Characterizing the proteome of biofluids with DIA-MS is an emerging area of interest for biomarker discovery; yet, there is limited consensus on DIA-MS data analysis approaches for analyzing large numbers of biofluids. To evaluate various DIA-MS workflows, we collected urine from a clinically heterogeneous cohort of prostate cancer patients and acquired data in DDA and DIA scan modes. We then searched the DIA data against urine spectral libraries generated using common library generation approaches or a library-free method. We show that DIA-MS doubles the sample throughput compared to standard DDA-MS with minimal losses to peptide detection. We further demonstrate that using a sample-specific spectral library generated from individual urines maximizes peptide detection compared to a library-free approach, a pan-human library, or libraries generated from pooled, fractionated urines. Adding urine subproteomes, such as the urinary extracellular vesicular proteome, to the urine spectral library further improves the detection of prostate proteins in unfractionated urine. Altogether, we present an optimized DIA-MS workflow and provide several high-quality, comprehensive prostate cancer urine spectral libraries that can streamline future biomarker discovery studies of prostate cancer using DIA-MS.

MassSpecPreppy-An end-to-end solution for automated protein concentration determination and flexible sample digestion for proteomics applications

In proteomics, fast, efficient, and highly reproducible sample preparation is of utmost importance, particularly in view of fast scanning mass spectrometers enabling analyses of large sample series. To address this need, we have developed the web application MassSpecPreppy that operates on the open science OT-2 liquid handling robot from Opentrons. This platform can prepare up to 96 samples at once, performing tasks like BCA protein concentration determination, sample digestion with normalization, reduction/alkylation and peptide elution into vials or loading specified peptide amounts onto Evotips in an automated and flexible manner. The performance of the developed workflows using MassSpecPreppy was compared with standard manual sample preparation workflows. The BCA assay experiments revealed an average recovery of 101.3% (SD: ± 7.82%) for the MassSpecPreppy workflow, while the manual workflow had a recovery of 96.3% (SD: ± 9.73%). The species mix used in the evaluation experiments showed that 94.5% of protein groups for OT-2 digestion and 95% for manual digestion passed the significance thresholds with comparable peptide level coefficient of variations. These results demonstrate that MassSpecPreppy is a versatile and scalable platform for automated sample preparation, producing injection-ready samples for proteomics research.

Multiplexed quantitative proteomics in prostate cancer biomarker development

Prostate cancer (PCa) is the most common non-skin cancer among men in the United States. However, the widely used protein biomarker in PCa, prostate-specific antigen (PSA), while useful for initial detection, its use alone cannot detect aggressive PCa and can lead to overtreatment. This chapter provides an overview of PCa protein biomarker development. It reviews the state-of-the-art liquid chromatography-mass spectrometry-based proteomics technologies for PCa biomarker development, such as enhancing the detection sensitivity of low-abundance proteins through antibody-based or antibody-independent protein/peptide enrichment, enriching post-translational modifications such as glycosylation as well as information-rich extracellular vesicles, and increasing accuracy and throughput using advanced data acquisition methodologies. This chapter also summarizes recent PCa biomarker validation studies that applied those techniques in diverse specimen types, including cell lines, tissues, proximal fluids, urine, and blood, developing novel protein biomarkers for various clinical applications, including early detection and diagnosis, prognosis, and therapeutic intervention of PCa.

Proteomics-The State of the Field: The Definition and Analysis of Proteomes Should Be Based in Reality, Not Convenience

With growing recognition and acknowledgement of the genuine complexity of proteomes, we are finally entering the post-proteogenomic era. Routine assessment of proteomes as inferred correlates of gene sequences (i.e., canonical 'proteins') cannot provide the necessary critical analysis of systems-level biology that is needed to understand underlying molecular mechanisms and pathways or identify the most selective biomarkers and therapeutic targets. These critical requirements demand the analysis of proteomes at the level of proteoforms/protein species, the actual active molecular players. Currently, only highly refined integrated or integrative top-down proteomics (iTDP) enables the analytical depth necessary to provide routine, comprehensive, and quantitative proteome assessments across the widest range of proteoforms inherent to native systems. Here we provide a broad perspective of the field, taking in historical and current realities, to establish a more balanced understanding of where the field has come from (in particular during the ten years since Proteomes was launched), current issues, and how things likely need to proceed if necessary deep proteome analyses are to succeed. We base this in our firm belief that the best proteomic analyses reflect, as closely as possible, the native sample at the moment of sampling. We also seek to emphasise that this and future analytical approaches are likely best based on the broad recognition and exploitation of the complementarity of currently successful approaches. This also emphasises the need to continuously evaluate and further optimize established approaches, to avoid complacency in thinking and expectations but also to promote the critical and careful development and introduction of new approaches, most notably those that address proteoforms. Above all, we wish to emphasise that a rigorous focus on analytical quality must override current thinking that largely values analytical speed; the latter would certainly be nice, if only proteoforms could thus be effectively, routinely, and quantitatively assessed. Alas, proteomes are composed of proteoforms, not molecular species that can be amplified or that directly mirror genes (i.e., 'canonical'). The problem is hard, and we must accept and address it as such, but the payoff in playing this longer game of rigorous deep proteome analyses is the promise of far more selective biomarkers, drug targets, and truly personalised or even individualised medicine.

Profiling Protein-Protein Interactions in the Human Brain by Refined Cofractionation Mass Spectrometry

Proteins usually execute their biological functions through interactions with other proteins and by forming macromolecular complexes, but global profiling of protein complexes directly from human tissue samples has been limited. In this study, we utilized cofractionation mass spectrometry (CF-MS) to map protein complexes within the postmortem human brain with experimental replicates. First, we used concatenated anion and cation Ion Exchange Chromatography (IEX) to separate native protein complexes in 192 fractions and then proceeded with Data-Independent Acquisition (DIA) mass spectrometry to analyze the proteins in each fraction, quantifying a total of 4,804 proteins with 3,260 overlapping in both replicates. We improved the DIA's quantitative accuracy by implementing a constant amount of bovine serum albumin (BSA) in each fraction as an internal standard. Next, advanced computational pipelines, which integrate both a database-based complex analysis and an unbiased protein-protein interaction (PPI) search, were applied to identify protein complexes and construct protein-protein interaction networks in the human brain. Our study led to the identification of 486 protein complexes and 10054 binary protein-protein interactions, which represents the first global profiling of human brain PPIs using CF-MS. Overall, this study offers a resource and tool for a wide range of human brain research, including the identification of disease-specific protein complexes in the future.

Proteomics of prostate cancer serum and plasma using low and high throughput approaches

Despite progress, MS-based proteomics in biofluids, especially blood, faces challenges such as dynamic range and throughput limitations in biomarker and disease studies. In this work, we used cutting-edge proteomics technologies to construct label-based and label-free workflows, capable of quantifying approximately 2,000 proteins in biofluids. With 70µL of blood and a single depletion strategy, we conducted an analysis of a homogenous cohort (n = 32), comparing medium-grade prostate cancer patients (Gleason score: 7(3 + 4); TNM stage: T2cN0M0, stage IIB) to healthy donors. The results revealed dozens of differentially expressed proteins in both plasma and serum. We identified the upregulation of Prostate Specific Antigen (PSA), a well-known biomarker for prostate cancer, in the serum of cancer cohort. Further bioinformatics analysis highlighted noteworthy proteins which appear to be differentially secreted into the bloodstream, making them good candidates for further exploration.

Analysis and visualization of quantitative proteomics data using FragPipe-Analyst

The FragPipe computational proteomics platform is gaining widespread popularity among the proteomics research community because of its fast processing speed and user-friendly graphical interface. Although FragPipe produces well-formatted output tables that are ready for analysis, there is still a need for an easy-to-use and user-friendly downstream statistical analysis and visualization tool. FragPipe-Analyst addresses this need by providing an R shiny web server to assist FragPipe users in conducting downstream analyses of the resulting quantitative proteomics data. It supports major quantification workflows including label-free quantification, tandem mass tags, and data-independent acquisition. FragPipe-Analyst offers a range of useful functionalities, such as various missing value imputation options, data quality control, unsupervised clustering, differential expression (DE) analysis using Limma, and gene ontology and pathway enrichment analysis using Enrichr. To support advanced analysis and customized visualizations, we also developed FragPipeAnalystR, an R package encompassing all FragPipe-Analyst functionalities that is extended to support site-specific analysis of post-translational modifications (PTMs). FragPipe-Analyst and FragPipeAnalystR are both open-source and freely available.

Substantial downregulation of mitochondrial and peroxisomal proteins during acute kidney injury revealed by data-independent acquisition proteomics

Acute kidney injury (AKI) manifests as a major health concern, particularly for the elderly. Understanding AKI-related proteome changes is critical for prevention and development of novel therapeutics to recover kidney function and to mitigate the susceptibility for recurrent AKI or development of chronic kidney disease. In this study, mouse kidneys were subjected to ischemia-reperfusion injury, and the contralateral kidneys remained uninjured to enable comparison and assess injury-induced changes in the kidney proteome. A ZenoTOF 7600 mass spectrometer was optimized for data-independent acquisition (DIA) to achieve comprehensive protein identification and quantification. Short microflow gradients and the generation of a deep kidney-specific spectral library allowed for high-throughput, comprehensive protein quantification. Upon AKI, the kidney proteome was completely remodeled, and over half of the 3945 quantified protein groups changed significantly. Downregulated proteins in the injured kidney were involved in energy production, including numerous peroxisomal matrix proteins that function in fatty acid oxidation, such as ACOX1, CAT, EHHADH, ACOT4, ACOT8, and Scp2. Injured kidneys exhibited severely damaged tissues and injury markers. The comprehensive and sensitive kidney-specific DIA-MS assays feature high-throughput analytical capabilities to achieve deep coverage of the kidney proteome, and will serve as useful tools for developing novel therapeutics to remediate kidney function.

Data-Independent Acquisition Proteomics and N-Terminomics Methods Reveal Alterations in Mitochondrial Function and Metabolism in Ischemic-Reperfused Hearts

Myocardial ischemia-reperfusion (IR) (stunning) injury triggers changes in the proteome and degradome of the heart. Here, we utilize quantitative proteomics and comprehensive degradomics to investigate the molecular mechanisms of IR injury in isolated rat hearts. The control group underwent aerobic perfusion, while the IR injury group underwent 20 min of ischemia and 30 min of reperfusion to induce a stunning injury. As MMP-2 activation has been shown to contribute to myocardial injury, hearts also underwent IR injury with ARP-100, an MMP-2-preferring inhibitor, to dissect the contribution of MMP-2 to IR injury. Using data-independent acquisition (DIA) and mass spectroscopy, we quantified 4468 proteins in ventricular extracts, whereby 447 proteins showed significant alterations among the three groups. We then used subtiligase-mediated N-terminomic labeling to identify more than a hundred specific cleavage sites. Among these protease substrates, 15 were identified following IR injury. We identified alterations in numerous proteins involved in mitochondrial function and metabolism following IR injury. Our findings provide valuable insights into the biochemical mechanisms of myocardial IR injury, suggesting alterations in reactive oxygen/nitrogen species handling and generation, fatty acid metabolism, mitochondrial function and metabolism, and cardiomyocyte contraction.

Acquisition and Analysis of DIA-Based Proteomic Data: A Comprehensive Survey in 2023

Data-independent acquisition (DIA) mass spectrometry (MS) has emerged as a powerful technology for high-throughput, accurate, and reproducible quantitative proteomics. This review provides a comprehensive overview of recent advances in both the experimental and computational methods for DIA proteomics, from data acquisition schemes to analysis strategies and software tools. DIA acquisition schemes are categorized based on the design of precursor isolation windows, highlighting wide-window, overlapping-window, narrow-window, scanning quadrupole-based, and parallel accumulation-serial fragmentation-enhanced DIA methods. For DIA data analysis, major strategies are classified into spectrum reconstruction, sequence-based search, library-based search, de novo sequencing, and sequencing-independent approaches. A wide array of software tools implementing these strategies are reviewed, with details on their overall workflows and scoring approaches at different steps. The generation and optimization of spectral libraries, which are critical resources for DIA analysis, are also discussed. Publicly available benchmark datasets covering global proteomics and phosphoproteomics are summarized to facilitate performance evaluation of various software tools and analysis workflows. Continued advances and synergistic developments of versatile components in DIA workflows are expected to further enhance the power of DIA-based proteomics.

Alzheimer's Disease Biomarker Analysis Using Targeted Mass Spectrometry

Alzheimer's disease (AD) is characterized by several neuropathological changes, mainly extracellular amyloid aggregates (plaques), intraneuronal inclusions of phosphorylated tau (tangles), as well as neuronal and synaptic degeneration, accompanied by tissue reactions to these processes (astrocytosis and microglial activation) that precede neuronal network disturbances in the symptomatic phase of the disease. A number of biomarkers for these brain tissue changes have been developed, mainly using immunoassays. In this review, we discuss how targeted mass spectrometry (TMS) can be used to validate and further characterize classes of biomarkers reflecting different AD pathologies, such as tau- and amyloid-beta pathologies, synaptic dysfunction, lysosomal dysregulation, and axonal damage, and the prospect of using TMS to measure these proteins in clinical research and diagnosis. TMS advantages and disadvantages in relation to immunoassays are discussed, and complementary aspects of the technologies are discussed.

Unveiling diagnostic and therapeutic strategies for cervical cancer: biomarker discovery through proteomics approaches and exploring the role of cervical cancer stem cells

Cervical cancer (CC) is a major global health problem and leading cause of cancer deaths among women worldwide. Early detection through screening programs has reduced mortality; however, screening compliance remains low. Identifying non-invasive biomarkers through proteomics for diagnosis and monitoring response to treatment could improve patient outcomes. Here we review recent proteomics studies which have uncovered biomarkers and potential drug targets for CC. Additionally, we explore into the role of cervical cancer stem cells and their potential implications in driving CC progression and therapy resistance. Although challenges remain, proteomics has the potential to revolutionize the field of cervical cancer research and improve patient outcomes.

How Can Proteomics Help to Elucidate the Pathophysiological Crosstalk in Muscular Dystrophy and Associated Multi-System Dysfunction?

This perspective article is concerned with the question of how proteomics, which is a core technique of systems biology that is deeply embedded in the multi-omics field of modern bioresearch, can help us better understand the molecular pathogenesis of complex diseases. As an illustrative example of a monogenetic disorder that primarily affects the neuromuscular system but is characterized by a plethora of multi-system pathophysiological alterations, the muscle-wasting disease Duchenne muscular dystrophy was examined. Recent achievements in the field of dystrophinopathy research are described with special reference to the proteome-wide complexity of neuromuscular changes and body-wide alterations/adaptations. Based on a description of the current applications of top-down versus bottom-up proteomic approaches and their technical challenges, future systems biological approaches are outlined. The envisaged holistic and integromic bioanalysis would encompass the integration of diverse omics-type studies including inter- and intra-proteomics as the core disciplines for systematic protein evaluations, with sophisticated biomolecular analyses, including physiology, molecular biology, biochemistry and histochemistry. Integrated proteomic findings promise to be instrumental in improving our detailed knowledge of pathogenic mechanisms and multi-system dysfunction, widening the available biomarker signature of dystrophinopathy for improved diagnostic/prognostic procedures, and advancing the identification of novel therapeutic targets to treat Duchenne muscular dystrophy.

In-Depth Proteome Profiling of Small Extracellular Vesicles Isolated from Cancer Cell Lines and Patient Serum

Extracellular vesicle (EV) secretion has been observed in many types of both normal and tumor cells. EVs contain a variety of distinctive cargoes, allowing tumor-derived serum proteins in EVs to act as a minimally invasive method for clinical monitoring. We have undertaken a comprehensive study of the protein content of the EVs from several cancer cell lines using direct data-independent analysis. Several thousand proteins were detected, including many classic EV markers such as CD9, CD81, CD63, TSG101, and Syndecan-1, among others. We detected many distinctive cancer-specific proteins, including several known markers used in cancer detection and monitoring. We further studied the protein content of EVs from patient serum for both normal controls and pancreatic cancer and hepatocellular carcinoma. The EVs for these studies have been isolated by various methods for comparison, including ultracentrifugation and CD9 immunoaffinity column. Typically, 500-1000 proteins were identified, where most of them overlapped with the EV proteins identified from the cell lines studied. We were able to identify many of the cell-line EV protein markers in the serum EVs, in addition to the large numbers of proteins specific to pancreatic and HCC cancers.

Comparative proteomics analysis of kidney in chicken infected by infectious bronchitis virus

The gamma coronavirus infectious bronchitis virus (IBV) is known to cause an acute and highly contagious infectious disease in poultry. Here, this study aimed to investigate the impact of virulent or avirulent IBV infection on the avian host by conducting proteomics with data-independent acquisition mass spectrometry (DIA-MS) in the kidneys of IBV-infected chickens. The results revealed 267, 489, and 510 differentially expressed proteins (DEPs) in the chicken kidneys at 3, 5, and 7 days postinfection (dpi), respectively, when infected with the GD17/04 strain, which is a highly nephrogenic strain and belongs to the 4/91 genotype. In contrast, the attenuated 4/91 vaccine resulted in the identification of 144, 175, and 258 DEPs at 3, 5, and 7 dpi, respectively. Functional enrichment analyses indicated distinct expression profiles between the 2 IBV strains. Upon GD17/04 infection, metabolic pathways respond initially in the early stage (3 dpi) and immune-related signaling pathways respond in the middle and late stages (5 and 7 dpi). The 4/91 vaccine elicited a completely opposite response compared to the GD17/04 infection. Among all DEPs, 62 immune-related DEPs were focused on and found to be mainly enriched in the type I interferon (IFN-I) signaling pathway and involved in humoral and cellular immunity. Notably, key molecules in the IFN-I signaling pathway including MDA5, LGP2, and TBK1 may serve as regulatory targets of IBV. Overall, this study highlights similarities and discrepancies in the patterns of protein expression at different stages of infection with virulent and avirulent IBV strains, with the IFN-I signaling pathway emerging as a critical response to IBV infection.

Spatial Proteomics toward Subcellular Resolution by Coupling Deep Ultraviolet Laser Ablation with Nanodroplet Sample Preparation

Multiplexed molecular profiling of tissue microenvironments, or spatial omics, can provide critical insights into cellular functions and disease pathology. The coupling of laser microdissection with mass spectrometry-based proteomics has enabled deep and unbiased mapping of >1000 proteins. However, the throughput of laser microdissection is often limited due to tedious two-step procedures, sequential laser cutting, and sample collection. The two-step procedure also hinders the further improvement of spatial resolution to <10 μm as needed for subcellular proteomics. Herein, we developed a high-throughput and high-resolution spatial proteomics platform by seamlessly coupling deep ultraviolet (DUV) laser ablation (LA) with nanoPOTS (Nanodroplet Processing in One pot for Trace Samples)-based sample preparation. We demonstrated the DUV-LA system can quickly isolate and collect tissue samples at a throughput of ∼30 spots/min and a spatial resolution down to 2 μm from a 10 μm thick human pancreas tissue section. To improve sample recovery, we developed a proximity aerosol collection approach by placing DMSO droplets close to LA spots. We demonstrated the DUV-LA-nanoPOTS platform can detect an average of 1312, 1533, and 1966 proteins from ablation spots with diameters of 7, 13, and 19 μm, respectively. In a proof-of-concept study, we isolated and profiled two distinct subcellular regions of the pancreas tissue revealed by hematoxylin and eosin (H&E) staining. Quantitative proteomics revealed proteins specifically enriched to subcellular compartments.

Data-independent acquisition (DIA) mass spectrometry reveals related proteins involved in the occurrence of early intestinal-type gastric cancer

Identifying proteins associated with the onset of early intestinal-type gastric cancer (EIGC) can yield valuable insights into the pathogenesis of this specific subtype of gastric cancer. Data-independent acquisition mass spectroscopy (DIA-MS) was utilized to identify the differential protein between 10 cases of EIGC and atrophic gastritis with intestinal metaplasia (NGC). The expressions of IPO4, TBL1XR1, p62/SQSTM1, PKP3, and CRTAP were verified by immunohistochemistry (IHC) in 20 EIGC samples, 17 gastric low-grade intraepithelial neoplasia (LGIN) samples, and 21 healthy controls. The prognostic values of the five genes were validated in the transcriptome data by survival analysis. A total of 4,028 proteins were identified using DIA-MS and a total of 177 differential proteins were screened with log2(fold change) > 1.5. Among them, 113 proteins were significantly up-regulated, and 64 proteins were significantly down-regulated in EIGC tissues. IHC results showed that proteins IPO4, TBL1XR1, p62/SQSTM1, PKP3, and CRTAP were highly expressed in the cytoplasm of EIGC and LGIN, which was consistent with the results of DIA-MS. Among them, p62/SQSTM1 may undergo nuclear-cytoplasmic transfer. The five protein-coding genes were associated with intestinal-type gastric cancer survival and exhibited differential expression across various disease stages. The study successfully identified differentially expressed proteins between EIGC and NGC, providing potential biomarkers and valuable insights into the mechanism underlying intestinal-type gastric cancer.

An integrated workflow for quantitative analysis of the newly synthesized proteome

The analysis of proteins that are newly synthesized upon a cellular perturbation can provide detailed insight into the proteomic response that is elicited by specific cues. This can be investigated by pulse-labeling of cells with clickable and stable-isotope-coded amino acids for the enrichment and mass spectrometric characterization of newly synthesized proteins (NSPs), however convoluted protocols prohibit their routine application. Here we report the optimization of multiple steps in sample preparation, mass spectrometry and data analysis, and we integrate them into a semi-automated workflow for the quantitative analysis of the newly synthesized proteome (QuaNPA). Reduced input requirements and data-independent acquisition (DIA) enable the analysis of triple-SILAC-labeled NSP samples, with enhanced throughput while featuring high quantitative accuracy. We apply QuaNPA to investigate the time-resolved cellular response to interferon-gamma (IFNg), observing rapid induction of targets 2 h after IFNg treatment. QuaNPA provides a powerful approach for large-scale investigation of NSPs to gain insight into complex cellular processes.

Recent advances in mass spectrometry-based methods to investigate reversible cysteine oxidation

The post-translational modification of cysteine to diverse oxidative states is understood as a critical cellular mechanism to combat oxidative stress. To study the role of cysteine oxidation, cysteine enrichments and subsequent analysis via mass spectrometry are necessary. As such, technologies and methods are rapidly developing for sensitive and efficient enrichments of cysteines to further explore its role in signaling pathways. In this review, we analyze recent developments in methods to miniaturize cysteine enrichments, analyze the underexplored disulfide bound redoxome, and quantify site-specific cysteine oxidation. We predict that further development of these methods will improve cysteine coverage across more diverse organisms than those previously studied and elicit novel roles cysteines play in stress response.

CysQuant: Simultaneous quantification of cysteine oxidation and protein abundance using data dependent or independent acquisition mass spectrometry

Protein cysteinyl thiols are susceptible to reduction-oxidation reactions that can influence protein function. Accurate quantification of cysteine oxidation is therefore crucial for decoding protein redox regulation. Here, we present CysQuant, a novel approach for simultaneous quantification of cysteine oxidation degrees and protein abundancies. CysQuant involves light/heavy iodoacetamide isotopologues for differential labeling of reduced and reversibly oxidized cysteines analyzed by data-dependent acquisition (DDA) or data-independent acquisition mass spectrometry (DIA-MS). Using plexDIA with in silico predicted spectral libraries, we quantified an average of 18% cysteine oxidation in Arabidopsis thaliana by DIA-MS, including a subset of highly oxidized cysteines forming disulfide bridges in AlphaFold2 predicted structures. Applying CysQuant to Arabidopsis seedlings exposed to excessive light, we successfully quantified the well-established increased reduction of Calvin-Benson cycle enzymes and discovered yet uncharacterized redox-sensitive disulfides in chloroplastic enzymes. Overall, CysQuant is a highly versatile tool for assessing the cysteine modification status that can be widely applied across various mass spectrometry platforms and organisms.

Mass Spectrometry-Based Proteomic Technology and Its Application to Study Skeletal Muscle Cell Biology

Voluntary striated muscles are characterized by a highly complex and dynamic proteome that efficiently adapts to changed physiological demands or alters considerably during pathophysiological dysfunction. The skeletal muscle proteome has been extensively studied in relation to myogenesis, fiber type specification, muscle transitions, the effects of physical exercise, disuse atrophy, neuromuscular disorders, muscle co-morbidities and sarcopenia of old age. Since muscle tissue accounts for approximately 40% of body mass in humans, alterations in the skeletal muscle proteome have considerable influence on whole-body physiology. This review outlines the main bioanalytical avenues taken in the proteomic characterization of skeletal muscle tissues, including top-down proteomics focusing on the characterization of intact proteoforms and their post-translational modifications, bottom-up proteomics, which is a peptide-centric method concerned with the large-scale detection of proteins in complex mixtures, and subproteomics that examines the protein composition of distinct subcellular fractions. Mass spectrometric studies over the last two decades have decisively improved our general cell biological understanding of protein diversity and the heterogeneous composition of individual myofibers in skeletal muscles. This detailed proteomic knowledge can now be integrated with findings from other omics-type methodologies to establish a systems biological view of skeletal muscle function.

Deep Proteome of the Developing Chick Midbrain

The epithelial-to-mesenchymal transition (EMT) and migration of cranial neural crest cells within the midbrain are critical processes that permit proper craniofacial patterning in the early embryo. Disruptions in these processes not only impair development but also lead to various diseases, underscoring the need for their detailed understanding at the molecular level. The chick embryo has served historically as an excellent model for human embryonic development, including cranial neural crest cell EMT and migration. While these developmental events have been characterized transcriptionally, studies at the protein level have not been undertaken to date. Here, we applied mass spectrometry (MS)-based proteomics to establish a deep proteomics profile of the chick midbrain region during early embryonic development. Our proteomics method combines optimal lysis conditions, offline fractionation, separation on a nanopatterned stationary phase (μPAC) using nanoflow liquid chromatography, and detection using quadrupole-ion trap-Orbitrap tribrid high-resolution tandem MS. Identification of >5900 proteins and >450 phosphoproteins in this study marks the deepest coverage of the chick midbrain proteome to date. These proteins have known roles in pathways related to neural crest cell EMT and migration such as signaling, proteolysis/extracellular matrix remodeling, and transcriptional regulation. This study offers valuable insight into important developmental processes occurring in the midbrain region and demonstrates the utility of proteomics for characterization of tissue microenvironments during chick embryogenesis.

Precision cancer medicine: Concepts, current practice, and future developments

Precision cancer medicine is a multidisciplinary team effort that requires involvement and commitment of many stakeholders including the society at large. Building on the success of significant advances in precision therapy for oncological patients over the last two decades, future developments will be significantly shaped by improvements in scalable molecular diagnostics in which increasingly complex multilayered datasets require transformation into clinically useful information guiding patient management at fast turnaround times. Adaptive profiling strategies involving tissue- and liquid-based testing that account for the immense plasticity of cancer during the patient's journey and also include early detection approaches are already finding their way into clinical routine and will become paramount. A second major driver is the development of smart clinical trials and trial concepts which, complemented by real-world evidence, rapidly broaden the spectrum of therapeutic options. Tight coordination with regulatory agencies and health technology assessment bodies is crucial in this context. Multicentric networks operating nationally and internationally are key in implementing precision oncology in clinical practice and support developing and improving the ecosystem and framework needed to turn invocation into benefits for patients. The review provides an overview of the diagnostic tools, innovative clinical studies, and collaborative efforts needed to realize precision cancer medicine.

Recent applications of quantitative mass spectrometry in biopharmaceutical process development and manufacturing

Biopharmaceutical products have seen rapid growth over the past few decades and continue to dominate the global pharmaceutical market. Aligning with the quality by design (QbD) framework and realization, recent advances in liquid chromatography-mass spectrometry (LC-MS) instrumentation and related techniques have enhanced biopharmaceutical characterization capabilities and have supported an increased development of biopharmaceutical products. Beyond its routine qualitative characterization, the quantitative feature of LC-MS has unique applications in biopharmaceutical process development and manufacturing. This review describes the recent applications and implications of the advancement of quantitative MS methods in biopharmaceutical process development, and characterization of biopharmaceutical product, product-related variants, and process-related impurities. We also provide insights on the emerging applications of quantitative MS in the lifecycle of biopharmaceutical product development including quality control in the Good Manufacturing Practice (GMP) environment and process analytical technology (PAT) practices during process development and manufacturing. Through collaboration with instrument and software vendors and regulatory agencies, we envision broader adoption of phase-appropriate quantitative MS-based methods for the analysis of biopharmaceutical products, which in turn has the potential to enable manufacture of higher quality products for patients.

[Molecular tumor diagnostics as the driving force behind precision oncology]

Molecular pathological diagnostics plays a central role in personalized oncology and requires multidisciplinary teamwork. It is just as relevant for the individual patient who is being treated with an approved therapy method or an individual treatment attempt as it is for prospective clinical studies that require the identification of specific therapeutic target structures or complex biomarkers for study inclusion. It is also of crucial importance for the generation of real-world data, which is becoming increasingly important for drug development. Future developments will be significantly shaped by improvements in scalable molecular diagnostics, in which increasingly complex and multi-layered data sets must be quickly converted into clinically useful information. One focus will be on the development of adaptive diagnostic strategies in order to be able to depict the enormous plasticity of a cancer disease over time.

Comparison of Quantitative Mass Spectrometric Methods for Drug Target Identification by Thermal Proteome Profiling

Thermal proteome profiling (TPP) provides a powerful approach to studying proteome-wide interactions of small therapeutic molecules and their target and off-target proteins, complementing phenotypic-based drug screens. Detecting differences in thermal stability due to target engagement requires high quantitative accuracy and consistent detection. Isobaric tandem mass tags (TMTs) are used to multiplex samples and increase quantification precision in TPP analysis by data-dependent acquisition (DDA). However, advances in data-independent acquisition (DIA) can provide higher sensitivity and protein coverage with reduced costs and sample preparation steps. Herein, we explored the performance of different DIA-based label-free quantification approaches compared to TMT-DDA for thermal shift quantitation. Acute myeloid leukemia cells were treated with losmapimod, a known inhibitor of MAPK14 (p38α). Label-free DIA approaches, and particularly the library-free mode in DIA-NN, were comparable of TMT-DDA in their ability to detect target engagement of losmapimod with MAPK14 and one of its downstream targets, MAPKAPK3. Using DIA for thermal shift quantitation is a cost-effective alternative to labeled quantitation in the TPP pipeline.

MSBooster: improving peptide identification rates using deep learning-based features

Peptide identification in liquid chromatography-tandem mass spectrometry (LC-MS/MS) experiments relies on computational algorithms for matching acquired MS/MS spectra against sequences of candidate peptides using database search tools, such as MSFragger. Here, we present a new tool, MSBooster, for rescoring peptide-to-spectrum matches using additional features incorporating deep learning-based predictions of peptide properties, such as LC retention time, ion mobility, and MS/MS spectra. We demonstrate the utility of MSBooster, in tandem with MSFragger and Percolator, in several different workflows, including nonspecific searches (immunopeptidomics), direct identification of peptides from data independent acquisition data, single-cell proteomics, and data generated on an ion mobility separation-enabled timsTOF MS platform. MSBooster is fast, robust, and fully integrated into the widely used FragPipe computational platform.

Analysis of DIA proteomics data using MSFragger-DIA and FragPipe computational platform

Liquid chromatography (LC) coupled with data-independent acquisition (DIA) mass spectrometry (MS) has been increasingly used in quantitative proteomics studies. Here, we present a fast and sensitive approach for direct peptide identification from DIA data, MSFragger-DIA, which leverages the unmatched speed of the fragment ion indexing-based search engine MSFragger. Different from most existing methods, MSFragger-DIA conducts a database search of the DIA tandem mass (MS/MS) spectra prior to spectral feature detection and peak tracing across the LC dimension. To streamline the analysis of DIA data and enable easy reproducibility, we integrate MSFragger-DIA into the FragPipe computational platform for seamless support of peptide identification and spectral library building from DIA, data-dependent acquisition (DDA), or both data types combined. We compare MSFragger-DIA with other DIA tools, such as DIA-Umpire based workflow in FragPipe, Spectronaut, DIA-NN library-free, and MaxDIA. We demonstrate the fast, sensitive, and accurate performance of MSFragger-DIA across a variety of sample types and data acquisition schemes, including single-cell proteomics, phosphoproteomics, and large-scale tumor proteome profiling studies.

Extracellular Matrix Proteomics: The mdx-4cv Mouse Diaphragm as a Surrogate for Studying Myofibrosis in Dystrophinopathy

The progressive degeneration of the skeletal musculature in Duchenne muscular dystrophy is accompanied by reactive myofibrosis, fat substitution, and chronic inflammation. Fibrotic changes and reduced tissue elasticity correlate with the loss in motor function in this X-chromosomal disorder. Thus, although dystrophinopathies are due to primary abnormalities in the DMD gene causing the almost-complete absence of the cytoskeletal Dp427-M isoform of dystrophin in voluntary muscles, the excessive accumulation of extracellular matrix proteins presents a key histopathological hallmark of muscular dystrophy. Animal model research has been instrumental in the characterization of dystrophic muscles and has contributed to a better understanding of the complex pathogenesis of dystrophinopathies, the discovery of new disease biomarkers, and the testing of novel therapeutic strategies. In this article, we review how mass-spectrometry-based proteomics can be used to study changes in key components of the endomysium, perimysium, and epimysium, such as collagens, proteoglycans, matricellular proteins, and adhesion receptors. The mdx-4cv mouse diaphragm displays severe myofibrosis, making it an ideal model system for large-scale surveys of systematic alterations in the matrisome of dystrophic fibers. Novel biomarkers of myofibrosis can now be tested for their appropriateness in the preclinical and clinical setting as diagnostic, pharmacodynamic, prognostic, and/or therapeutic monitoring indicators.

Substantial Downregulation of Mitochondrial and Peroxisomal Proteins during Acute Kidney Injury revealed by Data-Independent Acquisition Proteomics

Acute kidney injury (AKI) manifests as a major health concern, particularly for the elderly. Understanding AKI-related proteome changes is critical for prevention and development of novel therapeutics to recover kidney function and to mitigate the susceptibility for recurrent AKI or development of chronic kidney disease. In this study, mouse kidneys were subjected to ischemia-reperfusion injury, and the contralateral kidneys remained uninjured to enable comparison and assess injury-induced changes in the kidney proteome. A fast-acquisition rate ZenoTOF 7600 mass spectrometer was introduced for data-independent acquisition (DIA) for comprehensive protein identification and quantification. Short microflow gradients and the generation of a deep kidney-specific spectral library allowed for high-throughput, comprehensive protein quantification. Upon AKI, the kidney proteome was completely remodeled, and over half of the 3,945 quantified protein groups changed significantly. Downregulated proteins in the injured kidney were involved in energy production, including numerous peroxisomal matrix proteins that function in fatty acid oxidation, such as ACOX1, CAT, EHHADH, ACOT4, ACOT8, and Scp2. Injured mice exhibited severely declined health. The comprehensive and sensitive kidney-specific DIA assays highlighted here feature high-throughput analytical capabilities to achieve deep coverage of the kidney proteome and will serve as useful tools for developing novel therapeutics to remediate kidney function.

Fiber-Type Shifting in Sarcopenia of Old Age: Proteomic Profiling of the Contractile Apparatus of Skeletal Muscles

The progressive loss of skeletal muscle mass and concomitant reduction in contractile strength plays a central role in frailty syndrome. Age-related neuronal impairments are closely associated with sarcopenia in the elderly, which is characterized by severe muscular atrophy that can considerably lessen the overall quality of life at old age. Mass-spectrometry-based proteomic surveys of senescent human skeletal muscles, as well as animal models of sarcopenia, have decisively improved our understanding of the molecular and cellular consequences of muscular atrophy and associated fiber-type shifting during aging. This review outlines the mass spectrometric identification of proteome-wide changes in atrophying skeletal muscles, with a focus on contractile proteins as potential markers of changes in fiber-type distribution patterns. The observed trend of fast-to-slow transitions in individual human skeletal muscles during the aging process is most likely linked to a preferential susceptibility of fast-twitching muscle fibers to muscular atrophy. Studies with senescent animal models, including mostly aged rodent skeletal muscles, have confirmed fiber-type shifting. The proteomic analysis of fast versus slow isoforms of key contractile proteins, such as myosin heavy chains, myosin light chains, actins, troponins and tropomyosins, suggests them as suitable bioanalytical tools of fiber-type transitions during aging.

CTSB promotes sepsis-induced acute kidney injury through activating mitochondrial apoptosis pathway

Background

Acute kidney injury is a common and severe complication of sepsis. Sepsis -induced acute kidney injury(S-AKI) is an independent risk factor for mortality among sepsis patients. However, the mechanisms of S-AKI are complex and poorly understand. Therefore, exploring the underlying mechanisms of S-AKI may lead to the development of therapeutic targets.

Method

A model of S-AKI was established in male C57BL/6 mice using cecal ligation and puncture (CLP). The data-independent acquisition (DIA)-mass spectrometry-based proteomics was used to explore the protein expression changes and analyze the key proteomics profile in control and CLP group. The methodology was also used to identify the key proteins and pathways. S-AKI in vitro was established by treating the HK-2 cells with lipopolysaccharide (LPS). Subsequently, the effect and mechanism of Cathepsin B (CTSB) in inducing apoptosis in HK-2 cells were observed and verified.

Results

The renal injury scores, serum creatinine, blood urea nitrogen, and kidney injury molecule 1 were higher in septic mice than in non-septic mice. The proteomic analysis identified a total of 449 differentially expressed proteins (DEPs). GO and KEGG analysis showed that DEPs were mostly enriched in lysosomal-related cell structures and pathways. CTSB and MAPK were identified as key proteins in S-AKI. Electron microscopy observed enlarged lysosomes, swelled and ruptured mitochondria, and cytoplasmic vacuolization in CLP group. TUNEL staining and CTSB activity test showed that the apoptosis and CTSB activity were higher in CLP group than in control group. In HK-2 cell injury model, the CTSB activity and mRNA expression were increased in LPS-treated cells. Acridine orange staining showed that LPS caused lysosomal membrane permeabilization (LMP). CA074 as an inhibitor of CTSB could effectively inhibit CTSB activity. CCK8 and Annexin V/PI staining results indicated that CA074 reversed LPS-induced apoptosis of HK-2 cells. The JC-1 and western blot results showed that LPS inhibited mitochondrial membrane potential and activated mitochondrial apoptosis pathway, which could be reversed by CA074.

Conclusions

LMP and CTSB contribute to pathogenesis of S-AKI. LPS treatment induced HK-2 cell injury by activating mitochondrial apoptosis pathway. Inhibition of CTSB might be a new therapeutic strategy to alleviate sepsis-induced acute kidney injury.

Proteomic insights into SARS-CoV-2 infection mechanisms, diagnosis, therapies and prognostic monitoring methods

At the end of 2019, the COVID-19 pandemic, caused by severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) infection, seriously damaged world public health security. Several protein markers associated with virus infection have been extensively explored to combat the ever-increasing challenge posed by SARS-CoV-2. The proteomics of COVID-19 deepened our understanding of viral particles and their mechanisms of host invasion, providing us with information on protein changes in host tissues, cells and body fluids following infection in COVID-19 patients. In this review, we summarize the proteomic studies of SARS-CoV-2 infection and review the current understanding of COVID-19 in terms of the quantitative and qualitative proteomics of viral particles and host entry factors from the perspective of protein pathological changes in the organism following host infection.